Current CNN models for crack detection are listed in Table REF , showing also the various methods that have been used to enhance the extracted features of the crack pattern. The sequential models include basic CNN [1]}, CNN with metaheuristics (CNN-M) [2]}, deep fully CNN (FCN) [3]}, Cracknet [4]}, Cracknet-V [5]}, and densely-connected CNN [6]}. Those hierarchical CNN models relevant to this work are DeepCrack [7]}, feature pyramid and hierarchical boosting network (FPHBN) [8]}, U-Net[9]}, CNN with naïve Bayesian data fusion (NB-CNN) [10]}, weakly-supervised DCNN (WS-ConvNet) [11]}, PGA-Net [12]} and SDD-Net [13]}. Methods used for feature enhancement include deconvolutional decoders (D), residual modules (RM), probabilistic representation (PR), and statistic post-processing (SP). In the encoder-decoder structure, the crack patterns are to be rescaled with key indices recorded. With the preservation of those coordinates, the detailed patterns of crack features can be reasonably refilled with deconvolutional decoding. In RM, a combination of the original and processed features can be used to provide a residual effect like with human eyes, i.e., remembering the silhouette of the object that has previously been observed. The following step is to compensate for missing patterns using this residual effect. Alternatively, the feature maps are converted into a representation in the probabilistic space PR. In this case, every pixel will be assigned a possibility score in the range of \((0,1)\)  to evaluate the likelihood of a crack. As a result, the prediction of the model becomes less overconfident on uncertainty and mislabels, contributing positively to the reduction of false-positive rate. The last approach SP using a global optimizer with statistic post-processing tools to extract the result from CNN. The accuracy of the detection can be improved by filtering out outliner labels with a total threshold or some verification mechanism.
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